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Entropy production
Entropy production
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Entropy production (or generation) is the amount of entropy which is produced during heat process to evaluate the efficiency of the process.

Rudolf Clausius

Short history

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Entropy is produced in irreversible processes. The importance of avoiding irreversible processes (hence reducing the entropy production) was recognized as early as 1824 by Carnot.[1] In 1865 Rudolf Clausius expanded his previous work from 1854[2] on the concept of "unkompensierte Verwandlungen" (uncompensated transformations), which, in our modern nomenclature, would be called the entropy production. In the same article in which he introduced the name entropy,[3] Clausius gives the expression for the entropy production for a cyclical process in a closed system, which he denotes by N, in equation (71) which reads

Here S is the entropy in the final state and S0 the entropy in the initial state; S0-S is the entropy difference for the backwards part of the process. The integral is to be taken from the initial state to the final state, giving the entropy difference for the forwards part of the process. From the context, it is clear that N = 0 if the process is reversible and N > 0 in case of an irreversible process.

First and second law

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Fig. 1 General representation of an inhomogeneous system that consists of a number of subsystems. The interaction of the system with the surroundings is through exchange of heat and other forms of energy, flow of matter, and changes of shape. The internal interactions between the various subsystems are of a similar nature and lead to entropy production.

The laws of thermodynamics apply to well-defined systems. Fig. 1 is a general representation of a thermodynamic system. We consider systems which, in general, are inhomogeneous. Heat and mass are transferred across the boundaries (nonadiabatic, open systems), and the boundaries are moving (usually through pistons). In our formulation we assume that heat and mass transfer and volume changes take place only separately at well-defined regions of the system boundary. The expression, given here, are not the most general formulations of the first and second law. E.g. kinetic energy and potential energy terms are missing and exchange of matter by diffusion is excluded.

The rate of entropy production, denoted by , is a key element of the second law of thermodynamics for open inhomogeneous systems which reads

Here S is the entropy of the system; Tk is the temperature at which the heat enters the system at heat flow rate ; represents the entropy flow into the system at position k, due to matter flowing into the system ( are the molar flow rate and mass flow rate and Smk and sk are the molar entropy (i.e. entropy per unit amount of substance) and specific entropy (i.e. entropy per unit mass) of the matter, flowing into the system, respectively); represents the entropy production rates due to internal processes. The subscript 'i' in refers to the fact that the entropy is produced due to irreversible processes. The entropy-production rate of every process in nature is always positive or zero. This is an essential aspect of the second law.

The Σ's indicate the algebraic sum of the respective contributions if there are more heat flows, matter flows, and internal processes.

In order to demonstrate the impact of the second law, and the role of entropy production, it has to be combined with the first law which reads

with U the internal energy of the system; the enthalpy flows into the system due to the matter that flows into the system (Hmk its molar enthalpy, hk the specific enthalpy (i.e. enthalpy per unit mass)), and dVk/dt are the rates of change of the volume of the system due to a moving boundary at position k while pk is the pressure behind that boundary; P represents all other forms of power application (such as electrical).

The first and second law have been formulated in terms of time derivatives of U and S rather than in terms of total differentials dU and dS where it is tacitly assumed that dt > 0. So, the formulation in terms of time derivatives is more elegant. An even bigger advantage of this formulation is, however, that it emphasizes that heat flow rate and power are the basic thermodynamic properties and that heat and work are derived quantities being the time integrals of the heat flow rate and the power respectively.

Examples of irreversible processes

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Entropy is produced in irreversible processes. Some important irreversible processes are:

The expression for the rate of entropy production in the first two cases will be derived in separate sections.

Fig.2 a: Schematic diagram of a heat engine. A heating power enters the engine at the high temperature TH, and is released at ambient temperature Ta. A power P is produced and the entropy production rate is .
b: Schematic diagram of a refrigerator. is the cooling power at the low temperature TL, and is released at ambient temperature. The power P is supplied and is the entropy production rate. The arrows define the positive directions of the flows of heat and power in the two cases. They are positive under normal operating conditions.

Performance of heat engines and refrigerators

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Most heat engines and refrigerators are closed cyclic machines.[4] In the steady state the internal energy and the entropy of the machines after one cycle are the same as at the start of the cycle. Hence, on average, dU/dt = 0 and dS/dt = 0 since U and S are functions of state. Furthermore, they are closed systems () and the volume is fixed (dV/dt = 0). This leads to a significant simplification of the first and second law:

and

The summation is over the (two) places where heat is added or removed.

Engines

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For a heat engine (Fig. 2a) the first and second law obtain the form

and

Here is the heat supplied at the high temperature TH, is the heat removed at ambient temperature Ta, and P is the power delivered by the engine. Eliminating gives

The efficiency is defined by

If the performance of the engine is at its maximum and the efficiency is equal to the Carnot efficiency

Refrigerators

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For refrigerators (Fig. 2b) holds

and

Here P is the power, supplied to produce the cooling power at the low temperature TL. Eliminating now gives

The coefficient of performance of refrigerators is defined by

If the performance of the cooler is at its maximum. The COP is then given by the Carnot coefficient of performance

Power dissipation

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In both cases we find a contribution which reduces the system performance. This product of ambient temperature and the (average) entropy production rate is called the dissipated power.

Equivalence with other formulations

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It is interesting to investigate how the above mathematical formulation of the second law relates with other well-known formulations of the second law.

We first look at a heat engine, assuming that . In other words: the heat flow rate is completely converted into power. In this case the second law would reduce to

Since and this would result in which violates the condition that the entropy production is always positive. Hence: No process is possible in which the sole result is the absorption of heat from a reservoir and its complete conversion into work. This is the Kelvin statement of the second law.

Now look at the case of the refrigerator and assume that the input power is zero. In other words: heat is transported from a low temperature to a high temperature without doing work on the system. The first law with P = 0 would give

and the second law then yields

or

Since and this would result in which again violates the condition that the entropy production is always positive. Hence: No process is possible whose sole result is the transfer of heat from a body of lower temperature to a body of higher temperature. This is the Clausius statement of the second law.

Expressions for the entropy production

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Heat flow

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In case of a heat flow rate from T1 to T2 (with ) the rate of entropy production is given by

If the heat flow is in a bar with length L, cross-sectional area A, and thermal conductivity κ, and the temperature difference is small

the entropy production rate is

Flow of mass

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In case of a volume flow rate from a pressure p1 to p2

For small pressure drops and defining the flow conductance C by we get

The dependences of on T1T2 and on p1p2 are quadratic.

This is typical for expressions of the entropy production rates in general. They guarantee that the entropy production is positive.

Entropy of mixing

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In this Section we will calculate the entropy of mixing when two ideal gases diffuse into each other. Consider a volume Vt divided in two volumes Va and Vb so that Vt = Va + Vb. The volume Va contains amount of substance na of an ideal gas a and Vb contains amount of substance nb of gas b. The total amount of substance is nt = na + nb. The temperature and pressure in the two volumes is the same. The entropy at the start is given by

When the division between the two gases is removed the two gases expand, comparable to a Joule–Thomson expansion. In the final state the temperature is the same as initially but the two gases now both take the volume Vt. The relation of the entropy of an amount of substance n of an ideal gas is

where CV is the molar heat capacity at constant volume and R is the molar gas constant. The system is an adiabatic closed system, so the entropy increase during the mixing of the two gases is equal to the entropy production. It is given by

As the initial and final temperature are the same, the temperature terms cancel, leaving only the volume terms. The result is

Introducing the concentration x = na/nt = Va/Vt we arrive at the well-known expression

Joule expansion

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The Joule expansion is similar to the mixing described above. It takes place in an adiabatic system consisting of a gas and two rigid vessels a and b of equal volume, connected by a valve. Initially, the valve is closed. Vessel a contains the gas while the other vessel b is empty. When the valve is opened, the gas flows from vessel a into b until the pressures in the two vessels are equal. The volume, taken by the gas, is doubled while the internal energy of the system is constant (adiabatic and no work done). Assuming that the gas is ideal, the molar internal energy is given by Um = CVT. As CV is constant, constant U means constant T. The molar entropy of an ideal gas, as function of the molar volume Vm and T, is given by

The system consisting of the two vessels and the gas is closed and adiabatic, so the entropy production during the process is equal to the increase of the entropy of the gas. So, doubling the volume with T constant gives that the molar entropy produced is

Microscopic interpretation

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The Joule expansion provides an opportunity to explain the entropy production in statistical mechanical (i.e., microscopic) terms. At the expansion, the volume that the gas can occupy is doubled. This means that, for every molecule there are now two possibilities: it can be placed in container a or b. If the gas has amount of substance n, the number of molecules is equal to nNA, where NA is the Avogadro constant. The number of microscopic possibilities increases by a factor of 2 per molecule due to the doubling of volume, so in total the factor is 2nNA. Using the well-known Boltzmann expression for the entropy

where k is the Boltzmann constant and Ω is the number of microscopic possibilities to realize the macroscopic state. This gives change in molar entropy of

So, in an irreversible process, the number of microscopic possibilities to realize the macroscopic state is increased by a certain factor.

Basic inequalities and stability conditions

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In this section we derive the basic inequalities and stability conditions for closed systems. For closed systems the first law reduces to

The second law we write as

For adiabatic systems so dS/dt ≥ 0. In other words: the entropy of adiabatic systems cannot decrease. In equilibrium the entropy is at its maximum. Isolated systems are a special case of adiabatic systems, so this statement is also valid for isolated systems.

Now consider systems with constant temperature and volume. In most cases T is the temperature of the surroundings with which the system is in good thermal contact. Since V is constant the first law gives . Substitution in the second law, and using that T is constant, gives

With the Helmholtz free energy, defined as

we get

If P = 0 this is the mathematical formulation of the general property that the free energy of systems with fixed temperature and volume tends to a minimum. The expression can be integrated from the initial state i to the final state f resulting in

where WS is the work done by the system. If the process inside the system is completely reversible the equality sign holds. Hence the maximum work, that can be extracted from the system, is equal to the free energy of the initial state minus the free energy of the final state.

Finally we consider systems with constant temperature and pressure and take P = 0. As p is constant the first laws gives

Combining with the second law, and using that T is constant, gives

With the Gibbs free energy, defined as

we get

Homogeneous systems

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In homogeneous systems the temperature and pressure are well-defined and all internal processes are reversible. Hence . As a result, the second law, multiplied by T, reduces to

With P = 0 the first law becomes

Eliminating and multiplying with dt gives

Since

with Gm the molar Gibbs free energy and μ the molar chemical potential we obtain the well-known result

Entropy production in stochastic processes

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Since physical processes can be described by stochastic processes, such as Markov chains and diffusion processes, entropy production can be defined mathematically in such processes.[5]

For a continuous-time Markov chain with instantaneous probability distribution and transition rate , the instantaneous entropy production rate is

The long-time behavior of entropy production is kept after a proper lifting of the process. This approach provides a dynamic explanation for the Kelvin statement and the Clausius statement of the second law of thermodynamics.[6]

Entropy production in diffusive-reactive system has also been studied, with interesting results emerging from diffusion, cross diffusion and reactions.[7]

For a continuous-time Gauss-Markov process, a multivariate Ornstein-Uhlenbeck process is a diffusion process defined by coupled linear Langevin equations of the form

, i.e., in vector and matrix notations,

The are Gaussian white noises such that i.e.,

The stationary covariance matrix reads

We can parametrize the matrices , , and by setting

Finally, the entropy production reads [8]

A recent application of this formula is demonstrated in neuroscience, where it has been shown that entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain.[9]

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Entropy production is the irreversible generation of within a arising from processes such as , viscous , conduction across finite differences, and chemical reactions, serving as a quantitative measure of the departure from reversibility as mandated by the second law of . This production is inherently non-negative, equaling zero only in idealized reversible processes and positive otherwise, reflecting the unavoidable of useful work into and the consequent increase in the total of the and its surroundings. In mathematical terms, the local production rate, often denoted as σ\sigma, is defined as the time derivative of the internal per unit volume due to irreversible mechanisms, σ=disdt0\sigma = \frac{d_i s}{dt} \geq 0, where ss is the (internal per unit volume). In the framework of , entropy production plays a central role in analyzing open systems far from equilibrium, where continuous exchanges of energy and matter with the environment sustain steady states characterized by positive but minimized entropy generation. Developed extensively by , this field employs the bilinear form of entropy production as σ=JkXk\sigma = \sum J_k X_k, summing over thermodynamic fluxes JkJ_k (e.g., ) conjugate to thermodynamic forces XkX_k (e.g., gradients), which ensures σ>0\sigma > 0 and underpins principles like minimum entropy production for linear regimes near equilibrium. Beyond classical applications in engineering—such as assessing efficiency losses in heat engines or flows—entropy production extends to complex phenomena, including in dissipative structures like Bénard cells or biological , where local entropy increase paradoxically enables global order in far-from-equilibrium conditions.

Introduction and Fundamentals

Definition and basic principles

Entropy production, often denoted as σ, represents the rate at which entropy is generated within a thermodynamic system due to irreversible processes. In an isolated system, where no matter or energy is exchanged with the surroundings, the entropy production is defined as the time derivative of the total entropy, σ = dS/dt ≥ 0, ensuring that the entropy can only increase or remain constant over time. This quantity captures the fundamental irreversibility inherent in natural thermodynamic processes, distinguishing them from idealized reversible ones. Reversible processes, which occur infinitely slowly and maintain the system in equilibrium at every stage, exhibit zero entropy production (σ = 0), as there is no net generation of entropy from internal mechanisms. In contrast, irreversible processes, such as those involving , conduction across finite differences, or unrestricted expansion, result in positive entropy production (σ > 0), leading to an overall increase in the system's . This distinction underscores the directional nature of thermodynamic evolution toward equilibrium states. The total change in entropy for any thermodynamic system can be expressed as the sum of exchanged and internally produced contributions: dS=dSe+dSi,dS = dS_e + dS_i, where dSedS_e accounts for the entropy transferred to or from the surroundings (typically δQ/T\delta Q / T for heat exchange at temperature TT), and dSidS_i is the internal entropy production, which satisfies dSi0dS_i \geq 0 with equality only for reversible processes. In rate form, the production term relates to σ, emphasizing that dSi=σdtdS_i = \sigma \, dt. Entropy production serves as a quantitative measure of energy dissipation and the associated loss of useful work potential, often termed "lost work," where the dissipated energy is proportional to TσT \sigma. This concept aligns with the second law of thermodynamics by quantifying the inevitable degradation of energy quality in real-world processes.

Relation to thermodynamic laws

Entropy production arises from the integration of the first law of thermodynamics, which expresses energy conservation as dU=δQ+δWdU = \delta Q + \delta W, with the second law, which states that the entropy of an isolated system cannot decrease, dS0dS \geq 0. In this framework, the second law is expressed through the Clausius inequality for a cyclic process: δQT0\oint \frac{\delta Q}{T} \leq 0, where equality holds for reversible processes and the inequality reflects irreversibility due to internal dissipation. Combining these laws for a closed system undergoing infinitesimal changes yields the entropy balance equation dS=δQT+diSdS = \frac{\delta Q}{T} + d_i S, where diS0d_i S \geq 0 is the irreversible entropy change, or production, arising from dissipative processes within the system. To derive the explicit form of entropy production, consider alongside the definition of entropy for reversible processes, TdS=δQrevT dS = \delta Q_\text{rev}. For irreversible cases, the actual δQ\delta Q differs from δQrev\delta Q_\text{rev}, leading to TdS=δQ+TσdtT dS = \delta Q + T \sigma \, dt, where σ0\sigma \geq 0 is the local entropy production rate per unit , and δQ\delta Q includes contributions from and work. Rearranging with and assuming only pressure-volume work δW=PdV\delta W = -P dV, the combined expression becomes TdS=dU+PdV+Tσdt,T dS = dU + P dV + T \sigma \, dt, with the term TσdtT \sigma \, dt quantifying the irreversible dissipation that ensures the second law's entropy increase. This form highlights entropy production as the bridge between conserved energy (first law) and directional entropy growth (second law), applicable under the local thermodynamic equilibrium assumption where properties like temperature are defined locally despite gradients. The Clausius inequality generalizes to non-equilibrium conditions by decomposing the total entropy change into exchanged and produced parts: ΔSδQT\Delta S \geq \int \frac{\delta Q}{T}, with equality for reversible paths and the difference δQTΔS0\int \frac{\delta Q}{T} - \Delta S \leq 0 representing integrated production over the process. In , this extends to open systems with flows, where the production rate σ\sigma is expressed as σ=kJkXk0\sigma = \sum_k J_k X_k \geq 0, with JkJ_k as thermodynamic fluxes (e.g., or matter flow) and XkX_k as conjugate forces (e.g., or gradients), ensuring the second law holds locally. This formalism was foundationalized by Onsager, who in derived reciprocal relations for linear non-equilibrium processes near equilibrium, linking fluxes and forces via Ji=jLijXjJ_i = \sum_j L_{ij} X_j with symmetric coefficients Lij=LjiL_{ij} = L_{ji}, derived from microscopic reversibility and applied to entropy production minimization in stationary states. These relations establish entropy production as a that governs coupled , such as thermoelectric effects, while upholding the second law through of the LijL_{ij} matrix.

Historical Development

Early concepts

The foundational ideas of entropy production emerged in the early through efforts to understand the limits of heat engines and the inherent waste in thermal processes. Sadi Carnot, in his 1824 publication Reflections on the Motive Power of Fire, analyzed ideal reversible engines operating between heat reservoirs, emphasizing that maximum work extraction requires infinitesimal temperature differences to avoid losses. He explicitly recognized irreversibilities in real-world operations, such as in mechanical parts, which dissipates motive power into , and uneven heat conduction across finite temperature gradients, which similarly degrades available without producing useful work. Carnot's insight—that these dissipative effects represent a fundamental barrier to perfect —laid the groundwork for quantifying energy unavailability, though he framed it within rather than modern . Rudolf Clausius advanced these concepts in the 1850s by integrating the conservation of energy with Carnot's principles, establishing entropy as a key measure of process inefficiency. In his 1850 memoir On the Moving Force of Heat, Clausius introduced the idea of a state function that tracks the transformation of heat into work, showing that for reversible cycles, the integral of heat transferred divided by temperature equals zero, while irreversible processes yield a positive value, indicating entropy increase. By 1865, in The Mechanical Theory of Heat, he formalized entropy (denoted S) as this state function, whose change in any process satisfies an inequality: equality holds for reversible paths, but real cycles produce excess entropy due to dissipative mechanisms like friction and spontaneous heat flow. This inequality encapsulated the second law's directive that entropy production signifies the degradation of usable energy in isolated systems. Lord Kelvin (William Thomson) complemented Clausius's work in the mid-19th century by focusing on the practical implications of for availability. In his 1851 paper On the Dynamical Theory of , Kelvin articulated the second law as the impossibility of converting entirely into work without compensatory effects elsewhere, introducing the notion of "unavailable "—that portion of thermal rendered useless for work due to irreversible . He illustrated this through examples like conduction in solids, where temperature equalization dissipates potential without external compensation, and in machinery, which converts into dispersed . Kelvin's 1848 proposal of an absolute temperature scale, grounded in Carnot efficiency, further highlighted how accumulates over time, leading to a universal tendency toward degradation in finite systems. By the late 19th century, these classical insights shifted thermodynamic inquiry from idealized equilibrium cycles toward the realities of ongoing irreversible processes, prompting early explorations beyond static state functions. Pioneers like James Clerk Maxwell and began incorporating kinetic theory to model dissipative phenomena such as and , revealing that entropy production arises continuously in non-isolated systems interacting with surroundings. This transition underscored the limitations of equilibrium-focused , setting the stage for analyzing dynamic, far-from-equilibrium behaviors without yet formalizing a comprehensive non-equilibrium framework.

Key contributions in non-equilibrium thermodynamics

In the early 20th century, made foundational contributions to by deriving reciprocal relations that connect phenomenological coefficients in the linear regime to entropy production. In his 1931 papers, Onsager demonstrated that for systems near equilibrium, the fluxes of irreversible processes, such as heat conduction and , are linearly related to thermodynamic forces, with the matrix of coefficients being symmetric, ensuring the entropy production remains positive definite. These relations, grounded in the principle of , provided a rigorous framework for quantifying entropy generation in open systems, influencing subsequent developments in transport theory. Building on Onsager's work, advanced the understanding of entropy production in the 1940s and through his formulation of local entropy production and the principle of minimum entropy production for steady states near equilibrium. In his 1947 study, Prigogine showed that the rate of entropy production in a can be expressed as a sum of local contributions from irreversible processes, such as and flows, allowing for a that highlights the dissipative nature of non-equilibrium states. By the , he established that in linear irreversible , steady-state solutions minimize the total entropy production under fixed boundary conditions, a that stabilizes near-equilibrium dynamics while underscoring the second law's role in open systems. This approach shifted focus from global to local descriptions, enabling analysis of dissipative structures in chemical reactions and . The 1960s and 1970s saw the emergence of extended irreversible thermodynamics (EIT), which addressed limitations of classical theory by incorporating fast-relaxing variables, such as viscous fluxes and heat fluxes, directly into the entropy function to model systems farther from equilibrium. Pioneered by Ingo Müller in his 1967 entropy balance formulation, EIT extends the Gibbs relation to include non-equilibrium contributions, yielding hyperbolic transport equations with finite propagation speeds, unlike the parabolic ones in standard thermodynamics. Subsequent developments by Müller and collaborators in the 1970s, including collaborations with Tommaso Ruggeri, applied EIT to relativistic fluids and polymers, demonstrating how entropy production governs relaxation times and non-Fourier heat conduction, thus bridging microscopic relaxation to macroscopic irreversibility. This framework proved essential for describing rapid transients in materials science and plasma physics. Post-2000 research has integrated entropy production concepts with complexity theory, particularly in far-from-equilibrium systems, revealing how dissipation drives and emergent behaviors. The maximum entropy production principle (MEPP), formalized by Roderick Dewar in 2003 and reviewed by Martyushev and Seleznev in 2006, posits that in steady states, systems maximize entropy production subject to constraints, contrasting Prigogine's minimum principle and explaining in biological and geophysical flows through variational optimization. More recently, Jeremy England's 2013 work on dissipation-driven links high entropy production rates to of self-replicating structures in non-equilibrium environments, providing a thermodynamic basis for evolutionary in chemical and biological systems. These integrations highlight entropy production as a selector for complex, adaptive dynamics in open systems, with applications from to .

Irreversible Processes and Examples

Common examples of irreversibility

One prominent example of irreversibility arises in mechanical systems where converts ordered into disordered , thereby producing . In such processes, the dissipative work done by generates that increases the of the system and its surroundings, in accordance with the second law of thermodynamics. This production is inherent to the viscous within the material, where molecular interactions resist relative motion and lead to a net increase in thermal disorder. Uncontrolled expansion of gases provides another classic illustration of entropy production, as seen in free expansion where a gas suddenly expands into a without performing work or exchanging . In this , the remains constant for an , but the volume increase leads to a positive change due to the greater number of accessible microstates, reflecting the irreversible mixing of the gas with the empty space. The Joule-Thomson effect, involving throttling through a porous plug, similarly demonstrates irreversibility for real gases, where pressure drop at constant results in generation from intermolecular forces and non-uniform flow. In chemical reactions proceeding away from equilibrium, entropy production occurs through the , which drives the reaction toward completion by favoring the direction that minimizes free energy. The affinity, defined as the negative sum of differences times stoichiometric coefficients, quantifies the thermodynamic force, and its coupling with yields a positive entropy production rate, ensuring the process's spontaneity. This dissipation manifests as release or other irreversible transformations, distinguishing non-equilibrium reactions from reversible ones at equilibrium. Diffusion across concentration gradients exemplifies irreversibility when solute particles spread spontaneously from high to low concentration regions without external work, leading to entropy production via the homogenization of the system. This process, governed by Fick's laws, involves random molecular motions that increase the positional disorder, with the entropy generation arising from the flux-affinity product in . Unlike controlled diffusion in membranes that might extract work, uncontrolled cases purely dissipate the gradient's as thermal entropy.

Entropy production in heat transfer and fluid flow

In heat transfer processes, entropy production arises primarily from thermal gradients driving irreversible heat conduction, as described by Fourier's law. The local heat flux q\mathbf{q} is given by q=κT\mathbf{q} = -\kappa \nabla T, where κ\kappa is the thermal conductivity and T\nabla T is the . The corresponding volumetric entropy production rate σ\sigma due to this conduction is σ=κT2(T)2\sigma = \frac{\kappa}{T^2} (\nabla T)^2, where TT is the absolute temperature. This expression, derived from the bilinear form of fluxes and forces in linear irreversible thermodynamics, quantifies the irreversibility as heat flows from higher to lower temperatures, generating at a rate proportional to the square of the gradient. In flows, entropy production is also significant due to viscous dissipation, which converts into through internal . For Newtonian fluids governed by the Navier-Stokes equations, the τ\boldsymbol{\tau} relates to the rate-of-strain tensor, and the dissipation function Φ\Phi captures the work done against viscous forces. The entropy production rate from viscosity is σv=ηTΦ\sigma_v = \frac{\eta}{T} \Phi, where η\eta is the dynamic and Φ=2(uixj+ujxi)uixj/2\Phi = 2 \left( \frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} \right) \frac{\partial u_i}{\partial x_j} / 2 ( implied), representing the squared gradients. This term highlights how shear and extensional flows, such as in pipe or channel flows, inevitably , limiting the of systems. When heat and mass occur simultaneously in non-isothermal flows, entropy production becomes more complex, involving coupled fluxes. In such systems, the total entropy production includes contributions from , viscous dissipation, and diffusive mass , often expressed as σ=κT2(T)2+ηTΦkJkμkT\sigma = \frac{\kappa}{T^2} (\nabla T)^2 + \frac{\eta}{T} \Phi - \sum_k \frac{J_k \cdot \nabla \mu_k}{T}, where JkJ_k and μk\mu_k are the mass and of kk. These couplings, analyzed through , show that temperature variations can enhance or suppress mass diffusion, leading to higher overall irreversibility in processes like or . In applications, such as boundary layers over surfaces, entropy production integrates both conduction and viscous effects, providing insights into aerodynamic drag and . For instance, in a laminar , the entropy generation peaks near the wall due to high and gradients, with total scaling as σdVRe1/2\int \sigma \, dV \propto Re^{-1/2}, where ReRe is the , emphasizing the trade-off between friction and losses. This analysis aids in optimizing designs like blades or heat exchangers by minimizing localized dissipation.

Thermodynamic Devices and Efficiency

Heat engines

In the , which serves as the theoretical benchmark for heat engines operating between a reservoir at ThT_h and a cold reservoir at TcT_c, all processes are reversible, resulting in zero net entropy production (σ=0\sigma = 0). This idealization assumes temperature differences during , absence of , and quasi-static operations, allowing the engine to achieve the maximum possible η=1Tc/Th\eta = 1 - T_c / T_h without generating entropy. Real heat engines, however, inevitably produce entropy (σ>0\sigma > 0) due to practical irreversibilities such as mechanical friction in moving parts and finite-rate heat transfer across temperature gradients. Friction dissipates mechanical energy into heat, increasing the entropy of the system, while finite heat transfer rates—necessary for finite-time operation—create temperature drops between reservoirs and the working fluid, leading to non-quasistatic processes that further elevate entropy production. These factors reduce the engine's efficiency below the Carnot limit, with entropy generation directly quantifying the thermodynamic losses. For specific cycles like the and Diesel engines used in internal , entropy production arises from both and internal processes. The total entropy production is given by σtotal=δQhThδQcTc,\sigma_\text{total} = \int \frac{\delta Q_h}{T_h} - \int \frac{\delta Q_c}{T_c}, where the integrals represent contributions during heat addition (δQh\delta Q_h at boundary temperature ThT_h) and rejection (δQc\delta Q_c at TcT_c), accounting for irreversibilities in and internal processes through the temperatures and heats. In the , constant-volume heat addition amplifies entropy production due to rapid temperature rises, while the Diesel cycle's constant-pressure introduces additional losses from incomplete mixing and heat losses. These contributions limit efficiencies to around 30-40% in practice, far below Carnot values. Endoreversible engine models address finite-time constraints by assuming reversible internal cycles but irreversible heat exchanges with reservoirs, attributing entropy production solely to conductive heat transfer across finite temperature differences. Optimizing for maximum power yield the Curzon-Ahlborn efficiency ηCA=1Tc/Th\eta_\text{CA} = 1 - \sqrt{T_c / T_h}
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